Predicting Molecular Energies of Small Organic Molecules With Multi-Fidelity Methods

Multi-fidelity methods in machine learning (ML) have seen increasing usage for the prediction of quantum chemical properties. These methods, such as $$ -ML and Multifidelity Machine Learning (MFML), have been shown to significantly reduce the computational cost of generating training data. This work implements and analyzes several multi-fidelity methods including $$ -ML and MFML for the prediction of electronic molecular energies at DLPNO-CCSD(T) level, that is, at the level of coupled cluster theory including single and double excitations and perturbative triples corrections. The models for small organic molecules are evaluated not only on the basis of accuracy of prediction, but also on efficiency in terms of the time-cost of generating training data. In addition, the models are evaluated for the prediction of energies for molecules sampled from a public dataset, in particular for atmospherically relevant molecules, isomeric compounds, and highly conjugated complex molecules.